Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fc3ed2e4a90>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fc3ed21f208>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=(None, image_height, image_width, image_channels))
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim))
    learning_rate = tf.placeholder(tf.float32, shape=())

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha = 0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
   
    with tf.variable_scope('discriminator', reuse=reuse):
        x = tf.layers.conv2d(images, 32, 5, 2,'same', use_bias=False, activation=None)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)
        
        x = tf.layers.conv2d(x, 64, 5, 2,'same', use_bias=False, activation=None)
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)
        
        x = tf.layers.conv2d(x, 128, 5, 2,'same', use_bias=False, activation=None)
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)

        x_flat = tf.reshape(x, (-1, 4*4*128))
        logits = tf.layers.dense(x_flat, 1, activation=None)
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True,alpha = 0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        x = tf.layers.dense(z, 3*3*512, use_bias=False, activation=None)
        x = tf.reshape(x, (-1, 3, 3, 512))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)
 
        
        x = tf.layers.conv2d_transpose(x, 256, 5, 2,'same', use_bias=False, activation=None)
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)
      
        
        x = tf.layers.conv2d_transpose(x, 128, 5, 2,'same', use_bias=False, activation=None)
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        x = tf.nn.dropout(x, 0.85)
     
        
        x = tf.layers.conv2d_transpose(x, out_channel_dim, 6, 2,'valid', activation=None)
        out = tf.tanh(x)
   
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                
                 # Change the range from (-0.5, 0.5) to (-1, 1) to be consistent with batch_z
                batch_images *= 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run([d_train_opt, g_train_opt], feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})

                if steps % 100 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)         
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.5



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.4444... Generator Loss: 6.2291
Epoch 1/2... Discriminator Loss: 0.6681... Generator Loss: 9.4944
Epoch 1/2... Discriminator Loss: 0.7419... Generator Loss: 1.6630
Epoch 1/2... Discriminator Loss: 0.9610... Generator Loss: 1.5156
Epoch 1/2... Discriminator Loss: 0.6865... Generator Loss: 2.2743
Epoch 1/2... Discriminator Loss: 0.5526... Generator Loss: 2.4271
Epoch 1/2... Discriminator Loss: 1.0590... Generator Loss: 1.8681
Epoch 1/2... Discriminator Loss: 0.5292... Generator Loss: 3.4217
Epoch 1/2... Discriminator Loss: 1.9014... Generator Loss: 6.7927
Epoch 1/2... Discriminator Loss: 0.6849... Generator Loss: 2.9444
Epoch 1/2... Discriminator Loss: 0.6868... Generator Loss: 2.5764
Epoch 1/2... Discriminator Loss: 0.4504... Generator Loss: 3.3375
Epoch 1/2... Discriminator Loss: 0.8763... Generator Loss: 1.4922
Epoch 1/2... Discriminator Loss: 1.1435... Generator Loss: 1.5771
Epoch 1/2... Discriminator Loss: 1.3590... Generator Loss: 0.7699
Epoch 1/2... Discriminator Loss: 0.6551... Generator Loss: 2.6323
Epoch 1/2... Discriminator Loss: 0.6126... Generator Loss: 4.5261
Epoch 1/2... Discriminator Loss: 0.9479... Generator Loss: 1.5810
Epoch 1/2... Discriminator Loss: 0.7971... Generator Loss: 3.1516
Epoch 1/2... Discriminator Loss: 0.7547... Generator Loss: 2.5372
Epoch 1/2... Discriminator Loss: 0.9257... Generator Loss: 1.7479
Epoch 1/2... Discriminator Loss: 0.7994... Generator Loss: 2.0664
Epoch 1/2... Discriminator Loss: 0.9801... Generator Loss: 1.2572
Epoch 1/2... Discriminator Loss: 0.9546... Generator Loss: 2.1964
Epoch 1/2... Discriminator Loss: 0.6393... Generator Loss: 2.4001
Epoch 1/2... Discriminator Loss: 0.7332... Generator Loss: 1.7966
Epoch 1/2... Discriminator Loss: 0.7543... Generator Loss: 2.3674
Epoch 1/2... Discriminator Loss: 0.8283... Generator Loss: 1.7670
Epoch 1/2... Discriminator Loss: 0.8002... Generator Loss: 2.6037
Epoch 1/2... Discriminator Loss: 0.9014... Generator Loss: 2.1158
Epoch 1/2... Discriminator Loss: 0.7261... Generator Loss: 2.6632
Epoch 1/2... Discriminator Loss: 0.6683... Generator Loss: 2.7204
Epoch 1/2... Discriminator Loss: 0.9995... Generator Loss: 1.4750
Epoch 1/2... Discriminator Loss: 0.8690... Generator Loss: 3.1655
Epoch 1/2... Discriminator Loss: 0.7238... Generator Loss: 2.2728
Epoch 1/2... Discriminator Loss: 0.8115... Generator Loss: 1.7601
Epoch 1/2... Discriminator Loss: 0.8244... Generator Loss: 2.8583
Epoch 1/2... Discriminator Loss: 0.8826... Generator Loss: 1.8852
Epoch 1/2... Discriminator Loss: 0.9880... Generator Loss: 1.6735
Epoch 1/2... Discriminator Loss: 1.2883... Generator Loss: 0.9234
Epoch 1/2... Discriminator Loss: 1.0500... Generator Loss: 1.3520
Epoch 1/2... Discriminator Loss: 0.9465... Generator Loss: 1.5549
Epoch 1/2... Discriminator Loss: 0.9581... Generator Loss: 1.5508
Epoch 1/2... Discriminator Loss: 1.6434... Generator Loss: 0.5222
Epoch 1/2... Discriminator Loss: 1.1450... Generator Loss: 1.3651
Epoch 1/2... Discriminator Loss: 0.9467... Generator Loss: 1.4814
Epoch 2/2... Discriminator Loss: 1.0118... Generator Loss: 1.0864
Epoch 2/2... Discriminator Loss: 0.9679... Generator Loss: 1.3256
Epoch 2/2... Discriminator Loss: 1.0679... Generator Loss: 1.4129
Epoch 2/2... Discriminator Loss: 0.9010... Generator Loss: 1.4426
Epoch 2/2... Discriminator Loss: 0.9892... Generator Loss: 1.9031
Epoch 2/2... Discriminator Loss: 1.5835... Generator Loss: 0.5359
Epoch 2/2... Discriminator Loss: 0.9608... Generator Loss: 1.4527
Epoch 2/2... Discriminator Loss: 0.9709... Generator Loss: 1.8396
Epoch 2/2... Discriminator Loss: 0.9931... Generator Loss: 1.6291
Epoch 2/2... Discriminator Loss: 1.1209... Generator Loss: 0.9179
Epoch 2/2... Discriminator Loss: 1.0755... Generator Loss: 1.5283
Epoch 2/2... Discriminator Loss: 1.1356... Generator Loss: 1.6539
Epoch 2/2... Discriminator Loss: 1.0292... Generator Loss: 1.8350
Epoch 2/2... Discriminator Loss: 1.2667... Generator Loss: 2.9554
Epoch 2/2... Discriminator Loss: 0.8127... Generator Loss: 1.9223
Epoch 2/2... Discriminator Loss: 0.9222... Generator Loss: 1.6242
Epoch 2/2... Discriminator Loss: 1.1214... Generator Loss: 2.3583
Epoch 2/2... Discriminator Loss: 1.0241... Generator Loss: 1.0979
Epoch 2/2... Discriminator Loss: 0.9563... Generator Loss: 1.4401
Epoch 2/2... Discriminator Loss: 0.8766... Generator Loss: 1.6374
Epoch 2/2... Discriminator Loss: 0.8937... Generator Loss: 1.6675
Epoch 2/2... Discriminator Loss: 1.0707... Generator Loss: 1.8242
Epoch 2/2... Discriminator Loss: 1.0162... Generator Loss: 1.5996
Epoch 2/2... Discriminator Loss: 0.9605... Generator Loss: 1.4243
Epoch 2/2... Discriminator Loss: 1.0061... Generator Loss: 1.4610
Epoch 2/2... Discriminator Loss: 1.1034... Generator Loss: 1.0162
Epoch 2/2... Discriminator Loss: 1.0354... Generator Loss: 1.4936
Epoch 2/2... Discriminator Loss: 1.1750... Generator Loss: 1.9380
Epoch 2/2... Discriminator Loss: 0.9929... Generator Loss: 1.3627
Epoch 2/2... Discriminator Loss: 1.2239... Generator Loss: 0.8167
Epoch 2/2... Discriminator Loss: 0.9955... Generator Loss: 1.7623
Epoch 2/2... Discriminator Loss: 1.0743... Generator Loss: 1.5691
Epoch 2/2... Discriminator Loss: 1.0699... Generator Loss: 1.5832
Epoch 2/2... Discriminator Loss: 1.0170... Generator Loss: 1.3789
Epoch 2/2... Discriminator Loss: 1.0660... Generator Loss: 1.1126
Epoch 2/2... Discriminator Loss: 1.1600... Generator Loss: 1.1820
Epoch 2/2... Discriminator Loss: 1.0376... Generator Loss: 1.2744
Epoch 2/2... Discriminator Loss: 1.0363... Generator Loss: 1.3549
Epoch 2/2... Discriminator Loss: 0.9834... Generator Loss: 1.4454
Epoch 2/2... Discriminator Loss: 1.4278... Generator Loss: 0.5968
Epoch 2/2... Discriminator Loss: 1.1394... Generator Loss: 1.0945
Epoch 2/2... Discriminator Loss: 1.0924... Generator Loss: 1.3136
Epoch 2/2... Discriminator Loss: 1.0561... Generator Loss: 1.3865
Epoch 2/2... Discriminator Loss: 1.0230... Generator Loss: 1.5748
Epoch 2/2... Discriminator Loss: 1.2690... Generator Loss: 0.8010
Epoch 2/2... Discriminator Loss: 1.0054... Generator Loss: 1.4001
Epoch 2/2... Discriminator Loss: 1.0941... Generator Loss: 1.6193

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0582... Generator Loss: 1.1935
Epoch 1/1... Discriminator Loss: 3.9506... Generator Loss: 0.0526
Epoch 1/1... Discriminator Loss: 2.7804... Generator Loss: 0.1516
Epoch 1/1... Discriminator Loss: 0.6499... Generator Loss: 2.2733
Epoch 1/1... Discriminator Loss: 1.1590... Generator Loss: 2.6393
Epoch 1/1... Discriminator Loss: 0.9035... Generator Loss: 1.4122
Epoch 1/1... Discriminator Loss: 1.6073... Generator Loss: 0.5104
Epoch 1/1... Discriminator Loss: 1.5557... Generator Loss: 3.9023
Epoch 1/1... Discriminator Loss: 0.7642... Generator Loss: 3.1754
Epoch 1/1... Discriminator Loss: 0.6745... Generator Loss: 2.0119
Epoch 1/1... Discriminator Loss: 0.8524... Generator Loss: 5.6042
Epoch 1/1... Discriminator Loss: 1.3162... Generator Loss: 0.5748
Epoch 1/1... Discriminator Loss: 0.7251... Generator Loss: 2.5226
Epoch 1/1... Discriminator Loss: 0.8633... Generator Loss: 2.3891
Epoch 1/1... Discriminator Loss: 0.6767... Generator Loss: 4.1446
Epoch 1/1... Discriminator Loss: 0.6735... Generator Loss: 1.7255
Epoch 1/1... Discriminator Loss: 0.8534... Generator Loss: 2.3202
Epoch 1/1... Discriminator Loss: 0.9897... Generator Loss: 1.0608
Epoch 1/1... Discriminator Loss: 0.8579... Generator Loss: 2.0893
Epoch 1/1... Discriminator Loss: 0.6759... Generator Loss: 1.8520
Epoch 1/1... Discriminator Loss: 1.3123... Generator Loss: 0.7664
Epoch 1/1... Discriminator Loss: 0.9513... Generator Loss: 1.1935
Epoch 1/1... Discriminator Loss: 0.8973... Generator Loss: 1.2929
Epoch 1/1... Discriminator Loss: 1.0493... Generator Loss: 3.0064
Epoch 1/1... Discriminator Loss: 1.1584... Generator Loss: 1.3612
Epoch 1/1... Discriminator Loss: 0.7475... Generator Loss: 2.0378
Epoch 1/1... Discriminator Loss: 1.0778... Generator Loss: 0.9219
Epoch 1/1... Discriminator Loss: 0.9077... Generator Loss: 1.4136
Epoch 1/1... Discriminator Loss: 1.0291... Generator Loss: 3.0785
Epoch 1/1... Discriminator Loss: 0.7636... Generator Loss: 1.2618
Epoch 1/1... Discriminator Loss: 1.0533... Generator Loss: 1.1056
Epoch 1/1... Discriminator Loss: 0.9390... Generator Loss: 1.5950
Epoch 1/1... Discriminator Loss: 1.1808... Generator Loss: 2.2387
Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.3005
Epoch 1/1... Discriminator Loss: 0.6971... Generator Loss: 3.1330
Epoch 1/1... Discriminator Loss: 1.1221... Generator Loss: 0.9488
Epoch 1/1... Discriminator Loss: 1.1519... Generator Loss: 0.6859
Epoch 1/1... Discriminator Loss: 0.7532... Generator Loss: 1.6667
Epoch 1/1... Discriminator Loss: 1.1382... Generator Loss: 2.1827
Epoch 1/1... Discriminator Loss: 0.7210... Generator Loss: 2.3191
Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 2.0870
Epoch 1/1... Discriminator Loss: 0.7869... Generator Loss: 1.6649
Epoch 1/1... Discriminator Loss: 1.2143... Generator Loss: 0.9936
Epoch 1/1... Discriminator Loss: 1.1607... Generator Loss: 3.1161
Epoch 1/1... Discriminator Loss: 0.9908... Generator Loss: 2.1054
Epoch 1/1... Discriminator Loss: 1.1981... Generator Loss: 0.9757
Epoch 1/1... Discriminator Loss: 1.4778... Generator Loss: 0.9805
Epoch 1/1... Discriminator Loss: 1.1959... Generator Loss: 1.3517
Epoch 1/1... Discriminator Loss: 0.9451... Generator Loss: 1.4886
Epoch 1/1... Discriminator Loss: 1.0111... Generator Loss: 1.3010
Epoch 1/1... Discriminator Loss: 1.1000... Generator Loss: 0.9631
Epoch 1/1... Discriminator Loss: 0.9203... Generator Loss: 1.7962
Epoch 1/1... Discriminator Loss: 0.7035... Generator Loss: 2.1606
Epoch 1/1... Discriminator Loss: 1.0132... Generator Loss: 1.4520
Epoch 1/1... Discriminator Loss: 0.8605... Generator Loss: 1.7015
Epoch 1/1... Discriminator Loss: 1.2456... Generator Loss: 1.9497
Epoch 1/1... Discriminator Loss: 1.2712... Generator Loss: 0.9941
Epoch 1/1... Discriminator Loss: 1.0768... Generator Loss: 1.2238
Epoch 1/1... Discriminator Loss: 1.0390... Generator Loss: 1.2267
Epoch 1/1... Discriminator Loss: 0.8925... Generator Loss: 2.0198
Epoch 1/1... Discriminator Loss: 1.6697... Generator Loss: 0.4509
Epoch 1/1... Discriminator Loss: 1.1303... Generator Loss: 0.9252
Epoch 1/1... Discriminator Loss: 1.3729... Generator Loss: 0.7542
Epoch 1/1... Discriminator Loss: 1.0347... Generator Loss: 1.2668
Epoch 1/1... Discriminator Loss: 1.3604... Generator Loss: 0.7044
Epoch 1/1... Discriminator Loss: 0.9379... Generator Loss: 1.9437
Epoch 1/1... Discriminator Loss: 1.0697... Generator Loss: 1.2664
Epoch 1/1... Discriminator Loss: 1.3083... Generator Loss: 1.4592
Epoch 1/1... Discriminator Loss: 0.9739... Generator Loss: 1.2725
Epoch 1/1... Discriminator Loss: 1.1564... Generator Loss: 1.5835
Epoch 1/1... Discriminator Loss: 1.0134... Generator Loss: 0.9386
Epoch 1/1... Discriminator Loss: 1.2238... Generator Loss: 2.4803
Epoch 1/1... Discriminator Loss: 0.8247... Generator Loss: 1.6942
Epoch 1/1... Discriminator Loss: 0.8931... Generator Loss: 1.5438
Epoch 1/1... Discriminator Loss: 1.2761... Generator Loss: 1.2698
Epoch 1/1... Discriminator Loss: 1.0687... Generator Loss: 1.0514
Epoch 1/1... Discriminator Loss: 1.2561... Generator Loss: 1.0330
Epoch 1/1... Discriminator Loss: 0.8652... Generator Loss: 1.4831
Epoch 1/1... Discriminator Loss: 0.9853... Generator Loss: 2.0919
Epoch 1/1... Discriminator Loss: 1.0470... Generator Loss: 1.0847
Epoch 1/1... Discriminator Loss: 1.6989... Generator Loss: 0.4234
Epoch 1/1... Discriminator Loss: 1.1523... Generator Loss: 1.4751
Epoch 1/1... Discriminator Loss: 0.8360... Generator Loss: 1.7854
Epoch 1/1... Discriminator Loss: 1.5264... Generator Loss: 0.8705
Epoch 1/1... Discriminator Loss: 0.8509... Generator Loss: 1.6974
Epoch 1/1... Discriminator Loss: 0.9686... Generator Loss: 1.2595
Epoch 1/1... Discriminator Loss: 1.1135... Generator Loss: 1.2195
Epoch 1/1... Discriminator Loss: 1.0742... Generator Loss: 2.2960
Epoch 1/1... Discriminator Loss: 1.0519... Generator Loss: 1.3108
Epoch 1/1... Discriminator Loss: 1.0483... Generator Loss: 1.4522
Epoch 1/1... Discriminator Loss: 1.0102... Generator Loss: 1.1251
Epoch 1/1... Discriminator Loss: 1.1107... Generator Loss: 0.9766
Epoch 1/1... Discriminator Loss: 1.3387... Generator Loss: 1.7855
Epoch 1/1... Discriminator Loss: 1.0881... Generator Loss: 1.1962
Epoch 1/1... Discriminator Loss: 1.1431... Generator Loss: 0.9521
Epoch 1/1... Discriminator Loss: 1.1728... Generator Loss: 1.2753
Epoch 1/1... Discriminator Loss: 1.1144... Generator Loss: 0.8020
Epoch 1/1... Discriminator Loss: 1.0712... Generator Loss: 1.2536
Epoch 1/1... Discriminator Loss: 1.2398... Generator Loss: 2.2856
Epoch 1/1... Discriminator Loss: 1.0242... Generator Loss: 1.1750
Epoch 1/1... Discriminator Loss: 1.3909... Generator Loss: 2.1658
Epoch 1/1... Discriminator Loss: 1.2269... Generator Loss: 0.8925
Epoch 1/1... Discriminator Loss: 0.9850... Generator Loss: 1.2101
Epoch 1/1... Discriminator Loss: 1.0717... Generator Loss: 1.2949
Epoch 1/1... Discriminator Loss: 1.4490... Generator Loss: 0.6058
Epoch 1/1... Discriminator Loss: 0.9928... Generator Loss: 1.0826
Epoch 1/1... Discriminator Loss: 1.4294... Generator Loss: 0.6728
Epoch 1/1... Discriminator Loss: 0.7871... Generator Loss: 1.5419
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.8132
Epoch 1/1... Discriminator Loss: 0.9886... Generator Loss: 1.4400
Epoch 1/1... Discriminator Loss: 1.0510... Generator Loss: 1.0912
Epoch 1/1... Discriminator Loss: 0.9146... Generator Loss: 1.4076
Epoch 1/1... Discriminator Loss: 1.1327... Generator Loss: 1.0433
Epoch 1/1... Discriminator Loss: 0.9378... Generator Loss: 1.9196
Epoch 1/1... Discriminator Loss: 1.1362... Generator Loss: 0.9365
Epoch 1/1... Discriminator Loss: 1.1189... Generator Loss: 1.3928
Epoch 1/1... Discriminator Loss: 0.9258... Generator Loss: 1.9174

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.